R/inventorymetricsCSL.R

In inventorize: Inventory Analytics, Pricing and Markdowns

#' inventorymetricsCSL
#'
#' calculating inventory metrics based on CYCLE SERVICE LEVEL.
#'
#' cycle service level is the desired no of times demand is compleltey fulfiiled from cycle stock,after cycle service level  is explicitly  calculated, cost per item short, cost per stock out event and item fill rate
#' are implicitly calculated.
#'
#' @param   csl numeric,required times of demand that is fullfilled from cycle stock
#' @param demand numeric,annual demand of the SKU.
#' @param standerddeviation numeric, annual standard  deviation
#' @param  quantity, numeric,quantity replinished every cycle.
#' @param  leadtime, numeric,leadtime in weeks
#' @param cost, numeric,cost of the SKU.
#' @param holdingrate numeric, holding rate per item per year.
#'
#' @param na.rm A logical indicating whether missing values should be removed
#' @importFrom stats dnorm
#' @importFrom stats lm
#' @importFrom stats median
#' @importFrom stats optim
#' @importFrom stats optimize
#' @importFrom stats pnorm
#' @importFrom stats ppois
#' @importFrom stats predict
#' @importFrom stats qnorm
#' @return a dataframe that contains
#' demand leadtime, sigmadl(standard deviation in leadtime), saftey factor k determined
#' based on item fillrate provided, unit normal loss function, expected units to be short, cycle service level, fill rate,implied cost
#' per stockout event, saftey stock and suggested reorder point.
#' @author "haytham omar  email: <haytham@rescaleanalytics.com>"
#' @note this is the second version of the inventorize package, all the fucntions are  without
#' any academic contribution from my side, the aim is to facilitate and ease much of the bookkeeping that is endured during stock analysis.
#' @export
#'
#' @examples
#' inventorymetricsCSL(csl=0.95,demand=20000,standerddeviation=1200,
#' quantity=4500,leadtime=3,cost=100,holdingrate=0.15,na.rm=TRUE)



inventorymetricsCSL <-
  function(csl,demand,standerddeviation,quantity,leadtime,cost,holdingrate,na.rm=TRUE){
    DL<- demand* leadtime/52
    sigmadl<- standerddeviation *sqrt(leadtime/52)
    holdingcost<- holdingrate*cost
    k<- qnorm(csl)
    gk<- dnorm(k,0,1)-(k*(1-pnorm(k)))
    eus<- gk*sigmadl
    fillrate<- 1- (eus/quantity)
    CIS<- (quantity*holdingcost)/(demand*(1-csl))
    CSOE<- exp(k^2/2)*(holdingcost*quantity*sigmadl*sqrt(2*pi))*(1/demand)
    safteystock<- k*sigmadl
    reorder_point<- k*sigmadl+DL
    return(data.frame(DL=DL,sigmadl=sigmadl,k=k,gk=gk,eus=eus,csl=csl,fillrate=fillrate,
                      CIS=CIS,CSOE=CSOE,safteystock=safteystock,reorder_point=reorder_point))
  }